Travelled to:1 × China
1 × France
1 × Italy
1 × United Kingdom
2 × USA
Collaborated with:J.Park A.Sampson L.Ceze D.Burger D.Mahajan B.Thwaites X.Zhang M.Naik W.Harris T.Cao X.Yang S.M.Blackburn K.S.McKinley Emmanuel Amaro Fatemehsadat Mireshghallah Mohammadkazem Taram Prakash Ramrakhyani A.Jalali D.M.Tullsen T.Moreau M.Wyse J.Nelson M.Oskin B.Robatmili D.Li M.S.S.Govindan A.Smith A.Putnam S.W.Keckler A.Yazdanbakhsh A.Nagendrakumar S.Sethuraman K.Ramkrishnan N.Ravindran R.Jariwala A.Rahimi K.Bazargan
Talks about:approxim (5) support (3) languag (3) architectur (2) program (2) hardwar (2) comput (2) crowdsourc (1) implement (1) distribut (1)
Person: Hadi Esmaeilzadeh
DBLP: Esmaeilzadeh:Hadi
Contributed to:
2015
2015
20152013
2012201120162020Wrote 8 papers:
- DATE-2015-YazdanbakhshMTP #approximate #design #hardware #named
- Axilog: language support for approximate hardware design (AY, DM, BT, JP, AN, SS, KR, NR, RJ, AR, HE, KB), pp. 812–817.
- ESEC-FSE-2015-ParkEZNH #approximate #composition #named #programming
- FlexJava: language support for safe and modular approximate programming (JP, HE, XZ, MN, WH), pp. 745–757.
- HPCA-2015-MoreauWNSECO #approximate #named #programmable
- SNNAP: Approximate computing on programmable SoCs via neural acceleration (TM, MW, JN, AS, HE, LC, MO), pp. 603–614.
- HPCA-2013-RobatmiliLEGSPBK #architecture #effectiveness #how #manycore #predict
- How to implement effective prediction and forwarding for fusable dynamic multicore architectures (BR, DL, HE, MSSG, AS, AP, DB, SWK), pp. 460–471.
- ASPLOS-2012-EsmaeilzadehSCB #approximate #architecture #programming
- Architecture support for disciplined approximate programming (HE, AS, LC, DB), pp. 301–312.
- ASPLOS-2011-EsmaeilzadehCXBM #hardware #performance #roadmap #scalability
- Looking back on the language and hardware revolutions: measured power, performance, and scaling (HE, TC, XY, SMB, KSM), pp. 319–332.
- ASPLOS-2016-ParkAMTE #approximate #crowdsourcing #named #quality #towards
- AxGames: Towards Crowdsourcing Quality Target Determination in Approximate Computing (JP, EA, DM, BT, HE), pp. 623–636.
- ASPLOS-2020-MireshghallahTR #learning #named #privacy
- Shredder: Learning Noise Distributions to Protect Inference Privacy (FM, MT, PR, AJ, DMT, HE), pp. 3–18.